像素还是位置？群组活动识别中模态的基准测试
Pixels or Positions? Benchmarking Modalities in Group Activity Recognition
AI总结：
本文提出SoccerNet-GAR数据集，通过2022年世界杯64场比赛的广播视频和玩家跟踪数据，评估视频和跟踪模态在群组活动识别中的性能，提出基于图神经网络的跟踪模型，取得更高准确率且更高效。
摘要：
群组活动识别（GAR）在视频模态上已被广泛研究，但其他模态如代理位置和轨迹（即跟踪）仍较少被探索。本文引入SoccerNet-GAR数据集，包含2022年世界杯64场比赛的广播视频和玩家跟踪数据，同步标注10类群组活动。定义统一评估协议，评估两种强单模态方法：视频基分类器和基于图神经网络的跟踪基分类器。本文提出的基于角色的图架构直接通过球员位置边编码战术结构。跟踪模型在平衡准确率上达到77.8%，优于视频基线的60.9%，且训练耗时减少7倍，参数减少479倍。
英文摘要：
Group Activity Recognition (GAR) is well studied on the video modality for surveillance and indoor team sports (e.g., volleyball, basketball). Yet, other modalities such as agent positions and trajectories over time, i.e. tracking, remain comparatively under-explored despite being compact, agent-centric signals that explicitly encode spatial interactions. Understanding whether pixel (video) or position (tracking) modalities leads to better group activity recognition is therefore important to drive further research on the topic. However, no standardized benchmark currently exists that aligns broadcast video and tracking data for the same group activities, leading to a lack of apples-to-apples comparison between these modalities for GAR. In this work, we introduce SoccerNet-GAR, a multimodal dataset built from the $64$ matches of the football World Cup 2022. Specifically, the broadcast videos and player tracking modalities for $87{,}939$ group activities are synchronized and annotated with $10$ categories. Furthermore, we define a unified evaluation protocol to benchmark two strong unimodal approaches: (i) competitive video-based classifiers and (ii) tracking-based classifiers leveraging graph neural networks. In particular, our novel role-aware graph architecture for tracking-based GAR directly encodes tactical structure through positional edges connecting players by their on-pitch roles. Our tracking model achieves $77.8\%$ balanced accuracy compared to $60.9\%$ for the best video baseline, while training with $7 \times$ less GPU hours and $479 \times$ fewer parameters ($180K$ vs. $86.3M$). This study provides new insights into the relative strengths of pixels and positions for group activity recognition in sports.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
